| Do Transformers Really Perform Bad for Graph Representation? C Ying, T Cai, S Luo, S Zheng, G Ke, D He, Y Shen, TY Liu NeurIPS 2021, 2021 | 1196 | 2021 |
| Dual learning for machine translation D He, Y Xia, T Qin, L Wang, N Yu, TY Liu, WY Ma Advances in neural information processing systems 29, 2016 | 1140 | 2016 |
| On layer normalization in the transformer architecture R Xiong, Y Yang, D He, K Zheng, S Zheng, C Xing, H Zhang, Y Lan, ... International Conference on Machine Learning, 10524-10533, 2020 | 947 | 2020 |
| A theoretical analysis of NDCG ranking measures Y Wang, L Wang, Y Li, D He, W Chen, TY Liu Proceedings of the 26th Annual Conference on Learning Theory (COLT 2013) 8, 6, 2013 | 715* | 2013 |
| Incorporating bert into neural machine translation J Zhu, Y Xia, L Wu, D He, T Qin, W Zhou, H Li, TY Liu ICLR 2020, 2020 | 466 | 2020 |
| Rethinking positional encoding in language pre-training G Ke, D He, TY Liu ICLR 2020, 2020 | 269 | 2020 |
| Multilingual neural machine translation with knowledge distillation X Tan, Y Ren, D He, T Qin, Z Zhao, TY Liu ICLR 2019, 2019 | 266 | 2019 |
| Representation degeneration problem in training natural language generation models J Gao, D He, X Tan, T Qin, L Wang, T Liu ICLR 2019, 2018 | 256 | 2018 |
| Invertible image rescaling M Xiao, S Zheng, C Liu, Y Wang, D He, G Ke, J Bian, Z Lin, TY Liu Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 251 | 2020 |
| Macer: Attack-free and scalable robust training via maximizing certified radius R Zhai, C Dan, D He, H Zhang, B Gong, P Ravikumar, CJ Hsieh, L Wang ICLR 2020, 2020 | 192 | 2020 |
| Understanding and improving transformer from a multi-particle dynamic system point of view Y Lu, Z Li, D He, Z Sun, B Dong, T Qin, L Wang, TY Liu arXiv preprint arXiv:1906.02762, 2019 | 189 | 2019 |
| Frage: Frequency-agnostic word representation C Gong, D He, X Tan, T Qin, L Wang, TY Liu Advances in Neural Information Processing Systems, 1334-1345, 2018 | 179 | 2018 |
| Graphnorm: A principled approach to accelerating graph neural network training T Cai, S Luo, K Xu, D He, T Liu, L Wang International Conference on Machine Learning, 1204-1215, 2021 | 176 | 2021 |
| Adversarially robust generalization just requires more unlabeled data R Zhai, T Cai, D He, C Dan, K He, J Hopcroft, L Wang arXiv preprint arXiv:1906.00555, 2019 | 165 | 2019 |
| Non-autoregressive machine translation with auxiliary regularization Y Wang, F Tian, D He, T Qin, CX Zhai, TY Liu AAAI 2019, 2019 | 159 | 2019 |
| Efficient training of bert by progressively stacking L Gong, D He, Z Li, T Qin, L Wang, T Liu International conference on machine learning, 2337-2346, 2019 | 154 | 2019 |
| Non-autoregressive neural machine translation with enhanced decoder input J Guo, X Tan, D He, T Qin, L Xu, TY Liu Proceedings of the AAAI conference on artificial intelligence 33 (01), 3723-3730, 2019 | 134 | 2019 |
| Layer-wise coordination between encoder and decoder for neural machine translation T He, X Tan, Y Xia, D He, T Qin, Z Chen, TY Liu Advances in Neural Information Processing Systems 31, 2018 | 129 | 2018 |
| Towards a deep and unified understanding of deep neural models in nlp C Guan, X Wang, Q Zhang, R Chen, D He, X Xie International conference on machine learning, 2454-2463, 2019 | 125 | 2019 |
| Fast structured decoding for sequence models Z Sun, Z Li, H Wang, D He, Z Lin, Z Deng Advances in Neural Information Processing Systems 32, 2019 | 122 | 2019 |
Tie-Yan LiuDistinguished Scientist, Microsoft Research AI4Science | IEEE Fellow | ACM Fellow | AAIA FellowVerified email at microsoft.com
